{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d0e95b95-a43e-449d-803b-037c8595d46d",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": []
   },
   "source": [
    "> 🚨 **WARNING** 🚨\n",
    ">\n",
    "> Many cells in this notebook will not show up when viewed on GitHub. Please view the HTML version of this notebook in the [docs](https://docs.rastervision.io/en/latest/usage/tutorials/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4651326-3758-432d-8efa-37023d07dfb3",
   "metadata": {},
   "source": [
    "# Reading vector data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eea3d0bc-a6f7-412a-9cd9-f5ffcf1b6906",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "400089dc-816a-4104-a7c0-92fffe5e288f",
   "metadata": {},
   "source": [
    "We will be accessing files on S3 in this notebook. Since those files are public, we set the `AWS_NO_SIGN_REQUEST` to tell `rasterio` to skip the sign-in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d91a0cc-5912-4341-9198-64ad18dc784e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%env AWS_NO_SIGN_REQUEST=YES"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28947d0c-383f-44e1-b2db-adf2ef5a70b8",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Reading vector data using `VectorSource`"
   ]
  },
  {
   "cell_type": "raw",
   "id": "eff50e02-7b30-4b4d-8fb5-1308077abb9c",
   "metadata": {
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. currentmodule:: rastervision.core.data\n",
    "\n",
    "The :class:`.VectorSource` is Raster Vision's abstraction for reading from a source of vector data. \n",
    "\n",
    "Besides reading the data, they can also convert geometries from map-coordinates to pixel-coordinates and perform some data cleaning such as removing empty geometries and splitting apart multi-part geometries (e.g. ``MultiPolygon`` etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97cf98c2-7c2d-4ad3-9da8-7288b5f9d1ac",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7702313b-77cb-43ed-861e-6f835349f946",
   "metadata": {
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    "One concrete implementation of it is the :class:`.GeoJSONVectorSource` which can read vector data from a GeoJSON file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc745512-4a5c-43be-adbe-bbf0c463af4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rastervision.core.data import GeoJSONVectorSource, RasterioCRSTransformer\n",
    "\n",
    "img_uri = 's3://azavea-research-public-data/raster-vision/examples/spacenet/RGB-PanSharpen_AOI_2_Vegas_img205.tif'\n",
    "label_uri = 's3://azavea-research-public-data/raster-vision/examples/spacenet/buildings_AOI_2_Vegas_img205.geojson'\n",
    "\n",
    "crs_transformer = RasterioCRSTransformer.from_uri(img_uri)\n",
    "vector_source = GeoJSONVectorSource(label_uri, crs_transformer)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ffe72386-b0a1-4de6-877d-49d726876372",
   "metadata": {
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. note::\n",
    "\n",
    "    Note the use of :class:`.RasterioCRSTransformer` above. This allows us to match map coordinates in the label GeoJSON file to pixel coordinates in the image file."
   ]
  },
  {
   "cell_type": "raw",
   "id": "873c9c3e-2132-430a-a5ff-4b67b88c17e8",
   "metadata": {
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    "We can read data from a :class:`.VectorSource` in three different formats:\n",
    "\n",
    "1. as a GeoJSON dict (:meth:`.VectorSource.get_geojson`)\n",
    "2. as Shapely geoms (:meth:`.VectorSource.get_geoms`)\n",
    "3. as a GeoPandas :class:`~geopandas.GeoDataFrame` (:meth:`.VectorSource.get_dataframe`)\n",
    "\n",
    "Each of these is shown in the following cells."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b2aa6ef-a06b-42b8-b9dd-12610705cb8b",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a26c099-9929-44da-82ed-dcb5bde07339",
   "metadata": {
    "tags": []
   },
   "source": [
    "### `.get_geojson()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ca611ff0-827b-46ac-97de-4cff85a4589a",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-09 19:52:27:rastervision.pipeline.file_system.utils: INFO - Downloading s3://azavea-research-public-data/raster-vision/examples/spacenet/buildings_AOI_2_Vegas_img205.geojson to /opt/data/tmp/cache/s3/azavea-research-public-data/raster-vision/examples/spacenet/buildings_AOI_2_Vegas_img205.geojson...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'type': 'Feature',\n",
       "  'geometry': {'type': 'Polygon',\n",
       "   'coordinates': (((552.0, 587.0),\n",
       "     (485.0, 587.0),\n",
       "     (485.0, 604.0),\n",
       "     (482.0, 604.0),\n",
       "     (482.0, 621.0),\n",
       "     (503.0, 621.0),\n",
       "     (503.0, 624.0),\n",
       "     (515.0, 624.0),\n",
       "     (515.0, 633.0),\n",
       "     (552.0, 633.0),\n",
       "     (552.0, 587.0)),)},\n",
       "  'properties': {'OBJECTID': 0,\n",
       "   'FID_VEGAS_': 0,\n",
       "   'Id': 0,\n",
       "   'FID_Vegas': 0,\n",
       "   'Name': 'None',\n",
       "   'AREA': 0.0,\n",
       "   'Shape_Leng': 0.0,\n",
       "   'Shape_Le_1': 0.0,\n",
       "   'SISL': 0.0,\n",
       "   'OBJECTID_1': 0,\n",
       "   'Shape_Le_2': 0.0,\n",
       "   'Shape_Le_3': 0.000625,\n",
       "   'Shape_Area': 0.0,\n",
       "   'partialBuilding': 0.0,\n",
       "   'partialDec': 1.0}},\n",
       " {'type': 'Feature',\n",
       "  'geometry': {'type': 'Polygon',\n",
       "   'coordinates': (((561.0, 533.0),\n",
       "     (562.0, 487.0),\n",
       "     (486.0, 486.0),\n",
       "     (485.0, 527.0),\n",
       "     (541.0, 528.0),\n",
       "     (541.0, 532.0),\n",
       "     (561.0, 533.0)),)},\n",
       "  'properties': {'OBJECTID': 0,\n",
       "   'FID_VEGAS_': 0,\n",
       "   'Id': 0,\n",
       "   'FID_Vegas': 0,\n",
       "   'Name': 'None',\n",
       "   'AREA': 0.0,\n",
       "   'Shape_Leng': 0.0,\n",
       "   'Shape_Le_1': 0.0,\n",
       "   'SISL': 0.0,\n",
       "   'OBJECTID_1': 0,\n",
       "   'Shape_Le_2': 0.0,\n",
       "   'Shape_Le_3': 0.000658,\n",
       "   'Shape_Area': 0.0,\n",
       "   'partialBuilding': 0.0,\n",
       "   'partialDec': 1.0}},\n",
       " {'type': 'Feature',\n",
       "  'geometry': {'type': 'Polygon',\n",
       "   'coordinates': (((553.0, 465.0),\n",
       "     (552.0, 430.0),\n",
       "     (485.0, 431.0),\n",
       "     (486.0, 449.0),\n",
       "     (482.0, 449.0),\n",
       "     (480.0, 449.0),\n",
       "     (480.0, 466.0),\n",
       "     (482.0, 466.0),\n",
       "     (509.0, 466.0),\n",
       "     (509.0, 474.0),\n",
       "     (553.0, 474.0),\n",
       "     (553.0, 465.0)),)},\n",
       "  'properties': {'OBJECTID': 0,\n",
       "   'FID_VEGAS_': 0,\n",
       "   'Id': 0,\n",
       "   'FID_Vegas': 0,\n",
       "   'Name': 'None',\n",
       "   'AREA': 0.0,\n",
       "   'Shape_Leng': 0.0,\n",
       "   'Shape_Le_1': 0.0,\n",
       "   'SISL': 0.0,\n",
       "   'OBJECTID_1': 0,\n",
       "   'Shape_Le_2': 0.0,\n",
       "   'Shape_Le_3': 0.000627,\n",
       "   'Shape_Area': 0.0,\n",
       "   'partialBuilding': 0.0,\n",
       "   'partialDec': 1.0}}]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geojson = vector_source.get_geojson()\n",
    "geojson['features'][:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4849b408-c769-4d49-8589-9d454c5a353d",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc24f355-73c4-464e-9885-068fc40054ec",
   "metadata": {},
   "source": [
    "### `.get_geoms()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a2259fb-a67e-441f-8574-402e1ca4c386",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_geoms(geoms: list, title=''):\n",
    "    from matplotlib import pyplot as plt\n",
    "    from matplotlib import patches as patches\n",
    "    import numpy as np\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(5, 5))\n",
    "    for g in geoms:\n",
    "        if g.geom_type == 'Polygon':\n",
    "            xy = np.array(g.exterior.coords)\n",
    "            patch = patches.Polygon(xy, color='#55cc77', alpha=0.5)\n",
    "            ax.add_patch(patch)\n",
    "            patch = patches.Polygon(xy, edgecolor='#005511', fill=None, alpha=1)\n",
    "            ax.add_patch(patch)\n",
    "        elif g.geom_type == 'LineString':\n",
    "            xy = np.array(g.buffer(1).exterior.coords)\n",
    "            patch = patches.Polygon(xy, color='#005511', alpha=0.8)\n",
    "            ax.add_patch(patch)\n",
    "        else:\n",
    "            raise NotImplementedError()\n",
    "    ax.set_title(title, fontsize=14)\n",
    "    ax.autoscale()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "629b64ef-315c-4711-b8b1-73d4877b55e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geoms = vector_source.get_geoms()\n",
    "plot_geoms(geoms)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaab448b-5bd6-442a-8b33-27f83ab8e3d9",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64698d01-6633-42ff-9cab-46cf91e28e8f",
   "metadata": {},
   "source": [
    "### `.get_dataframe()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6999beee-9cd6-49cf-8976-b72d4dfd2ed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>FID_VEGAS_</th>\n",
       "      <th>Id</th>\n",
       "      <th>FID_Vegas</th>\n",
       "      <th>Name</th>\n",
       "      <th>AREA</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Le_1</th>\n",
       "      <th>SISL</th>\n",
       "      <th>OBJECTID_1</th>\n",
       "      <th>Shape_Le_2</th>\n",
       "      <th>Shape_Le_3</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>partialBuilding</th>\n",
       "      <th>partialDec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((552.000 587.000, 485.000 587.000, 48...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((561.000 533.000, 562.000 487.000, 48...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((553.000 465.000, 552.000 430.000, 48...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000627</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((551.000 374.000, 493.000 375.000, 49...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((535.000 315.000, 468.000 315.000, 46...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry  OBJECTID  FID_VEGAS_  \\\n",
       "0  POLYGON ((552.000 587.000, 485.000 587.000, 48...         0           0   \n",
       "1  POLYGON ((561.000 533.000, 562.000 487.000, 48...         0           0   \n",
       "2  POLYGON ((553.000 465.000, 552.000 430.000, 48...         0           0   \n",
       "3  POLYGON ((551.000 374.000, 493.000 375.000, 49...         0           0   \n",
       "4  POLYGON ((535.000 315.000, 468.000 315.000, 46...         0           0   \n",
       "\n",
       "   Id  FID_Vegas  Name  AREA  Shape_Leng  Shape_Le_1  SISL  OBJECTID_1  \\\n",
       "0   0          0  None   0.0         0.0         0.0   0.0           0   \n",
       "1   0          0  None   0.0         0.0         0.0   0.0           0   \n",
       "2   0          0  None   0.0         0.0         0.0   0.0           0   \n",
       "3   0          0  None   0.0         0.0         0.0   0.0           0   \n",
       "4   0          0  None   0.0         0.0         0.0   0.0           0   \n",
       "\n",
       "   Shape_Le_2  Shape_Le_3  Shape_Area  partialBuilding  partialDec  \n",
       "0         0.0    0.000625         0.0              0.0         1.0  \n",
       "1         0.0    0.000658         0.0              0.0         1.0  \n",
       "2         0.0    0.000627         0.0              0.0         1.0  \n",
       "3         0.0    0.000744         0.0              0.0         1.0  \n",
       "4         0.0    0.000634         0.0              0.0         1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = vector_source.get_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "194f646e-6af6-4931-9b2a-04db3349b1c5",
   "metadata": {},
   "source": [
    "<hr style=\"border:2px solid gray\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024fe3ab-412b-4242-849c-fc71dd986e78",
   "metadata": {},
   "source": [
    "## Transforming vector data using `VectorTransformer`s"
   ]
  },
  {
   "cell_type": "raw",
   "id": "8dd13d9f-d3c7-4f8b-93ef-d93d80c7ffe7",
   "metadata": {
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    "Just like we can transform rasters by specifying a series of :class:`RasterTransformers <raster_transformer.raster_transformer.RasterTransformer>`, we can transform vector data by specifying a series of :class:`VectorTransformers <vector_transformer.vector_transformer.VectorTransformer>`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122ac141-49ba-4ea6-9982-0233ee7faf80",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ae481a-45c0-401f-ba43-c4f9b78e6d59",
   "metadata": {},
   "source": [
    "### Inferring class IDs for polygons"
   ]
  },
  {
   "cell_type": "raw",
   "id": "eb379d6d-aced-4c00-8112-870ae749131d",
   "metadata": {
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    "One very important :class:`.VectorTransformer` is the :class:`.ClassInferenceTransformer`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb9e2be-fa0d-41fe-97ab-1d8e2d116fde",
   "metadata": {},
   "source": [
    "When using vector data in machine learning, it is important that each polygon be labeled with an appropriate class ID. But often, your data will not have this property stored in the GeoJSON file.\n",
    "\n",
    "The `ClassInferenceTransformer` can automatically infer and attach a `class_id` to each polygon read from the `VectorSource`. It can\n",
    "\n",
    "1. Assign the same `class_id` to all the polygons (a very common use case).\n",
    "2. Map class names to `class_id`s, given a mapping.\n",
    "3. Use a MapBox-style filter (see https://docs.mapbox.com/mapbox-gl-js/style-spec/other/#other-filter) for more complex rule-based ID assignment.\n",
    "\n",
    "The example below shows how to use the first of the above methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5945fba8-822e-4dd7-a6ee-f45b953fa05d",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from rastervision.core.data import (\n",
    "    GeoJSONVectorSource, RasterioCRSTransformer, \n",
    "    RasterizedSource, ClassInferenceTransformer)\n",
    "\n",
    "img_uri = 's3://azavea-research-public-data/raster-vision/examples/spacenet/RGB-PanSharpen_AOI_2_Vegas_img205.tif'\n",
    "label_uri = 's3://azavea-research-public-data/raster-vision/examples/spacenet/buildings_AOI_2_Vegas_img205.geojson'\n",
    "\n",
    "crs_transformer = RasterioCRSTransformer.from_uri(img_uri)\n",
    "vector_source = GeoJSONVectorSource(\n",
    "    label_uri,\n",
    "    crs_transformer,\n",
    "    vector_transformers=[ClassInferenceTransformer(default_class_id=1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ab9790b7-6654-497f-b8e0-bfe8d0a5631e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-09 19:53:12:rastervision.pipeline.file_system.utils: INFO - Using cached file /opt/data/tmp/cache/s3/azavea-research-public-data/raster-vision/examples/spacenet/buildings_AOI_2_Vegas_img205.geojson.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>class_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((552.000 587.000, 485.000 587.000, 48...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((561.000 533.000, 562.000 487.000, 48...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((553.000 465.000, 552.000 430.000, 48...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((551.000 374.000, 493.000 375.000, 49...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((535.000 315.000, 468.000 315.000, 46...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry  class_id\n",
       "0  POLYGON ((552.000 587.000, 485.000 587.000, 48...         1\n",
       "1  POLYGON ((561.000 533.000, 562.000 487.000, 48...         1\n",
       "2  POLYGON ((553.000 465.000, 552.000 430.000, 48...         1\n",
       "3  POLYGON ((551.000 374.000, 493.000 375.000, 49...         1\n",
       "4  POLYGON ((535.000 315.000, 468.000 315.000, 46...         1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = vector_source.get_dataframe()\n",
    "df[['geometry', 'class_id']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16948195-0084-4187-926d-30ace84e5096",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8919c455-163c-4e34-abbd-3ede3bc02d62",
   "metadata": {},
   "source": [
    "### Buffering Point and LineString geometries into polygons"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b9fd25bc-b9b0-4e31-b95e-0264b058cb9f",
   "metadata": {
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    "``Point`` and ``LineString`` geometries are not directly useable if doing, say, semantic segmentation. The cells below show an example of converting road geometries (given in the form of ``LineString``s) into polygons using the :class:`.BufferTransformer`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b76db21e-e124-4437-865a-7623fb1b35d3",
   "metadata": {},
   "source": [
    "Data source: https://spacenet.ai/spacenet-roads-dataset/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7d1709b9-2116-42ee-9753-e907018487b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rastervision.core.data import (\n",
    "    GeoJSONVectorSource, RasterioCRSTransformer, \n",
    "    RasterizedSource, BufferTransformer)\n",
    "\n",
    "img_uri = 's3://spacenet-dataset/spacenet/SN3_roads/train/AOI_4_Shanghai/PS-RGB/SN3_roads_train_AOI_4_Shanghai_PS-RGB_img999.tif'\n",
    "label_uri = 's3://spacenet-dataset/spacenet/SN3_roads/train/AOI_4_Shanghai/geojson_roads/SN3_roads_train_AOI_4_Shanghai_geojson_roads_img999.geojson'\n",
    "\n",
    "crs_transformer = RasterioCRSTransformer.from_uri(img_uri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5c8f83a-e4a8-4242-b8d0-fc016ad06e68",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_geoms(geoms: list, title=''):\n",
    "    from matplotlib import pyplot as plt\n",
    "    from matplotlib import patches as patches\n",
    "    import numpy as np\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(5, 5))\n",
    "    for g in geoms:\n",
    "        if g.geom_type == 'Polygon':\n",
    "            xy = np.array(g.exterior.coords)\n",
    "            patch = patches.Polygon(xy, color='#55cc77', alpha=0.5)\n",
    "            ax.add_patch(patch)\n",
    "            patch = patches.Polygon(xy, edgecolor='#005511', fill=None, alpha=1)\n",
    "            ax.add_patch(patch)\n",
    "        elif g.geom_type == 'LineString':\n",
    "            xy = np.array(g.buffer(1).exterior.coords)\n",
    "            patch = patches.Polygon(xy, color='#005511', alpha=0.8)\n",
    "            ax.add_patch(patch)\n",
    "        else:\n",
    "            raise NotImplementedError()\n",
    "    ax.set_title(title, fontsize=14)\n",
    "    ax.autoscale()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d3a8fd04-6ac6-43c5-b124-f7da2b7404bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-09 19:53:33:rastervision.pipeline.file_system.utils: INFO - Downloading s3://spacenet-dataset/spacenet/SN3_roads/train/AOI_4_Shanghai/geojson_roads/SN3_roads_train_AOI_4_Shanghai_geojson_roads_img999.geojson to /opt/data/tmp/cache/s3/spacenet-dataset/spacenet/SN3_roads/train/AOI_4_Shanghai/geojson_roads/SN3_roads_train_AOI_4_Shanghai_geojson_roads_img999.geojson...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vector_source = GeoJSONVectorSource(label_uri, crs_transformer)\n",
    "plot_geoms(vector_source.get_geoms(), title='Roads as LineStrings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "762642d8-5e6d-4c79-b4c2-f4abe567ba87",
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-09 19:53:40:rastervision.pipeline.file_system.utils: INFO - Using cached file /opt/data/tmp/cache/s3/spacenet-dataset/spacenet/SN3_roads/train/AOI_4_Shanghai/geojson_roads/SN3_roads_train_AOI_4_Shanghai_geojson_roads_img999.geojson.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vector_source_buffered = GeoJSONVectorSource(\n",
    "    label_uri,\n",
    "    crs_transformer,\n",
    "    vector_transformers=[BufferTransformer(geom_type='LineString', default_buf=10)])\n",
    "\n",
    "plot_geoms(vector_source_buffered.get_geoms(), title='Roads buffered into Polygons')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7af03d2e-b2f0-496c-841d-341dbbb5762f",
   "metadata": {},
   "source": [
    "<hr style=\"border:2px solid gray\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d1bcad9-f9f2-4961-a09a-280e86b1a5d6",
   "metadata": {},
   "source": [
    "## Rasterizing vector data using `RasterizedSource`"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9e349df7-22fa-4997-a8c1-845d479b5350",
   "metadata": {
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    "Suppose we have semantic segmentation labels in the form of polygons. To use them for training, we will first need to convert them into rasters. Raster Vision allows accomplishing this using the :class:`.RasterizedSource` class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e32dbfa8-180b-482c-b83e-24c556a13c14",
   "metadata": {},
   "source": [
    "The `RasterizedSource` is a `RasterSource` that reads data from a `VectorSource` (rather than an image file) and then converts it into rasters. It can be indexed like any other `RasterSource`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f9e5447-6491-49d0-8071-5efb2952f98b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-09 19:54:07:rastervision.pipeline.file_system.utils: INFO - Using cached file /opt/data/tmp/cache/s3/azavea-research-public-data/raster-vision/examples/spacenet/buildings_AOI_2_Vegas_img205.geojson.\n"
     ]
    }
   ],
   "source": [
    "from rastervision.core.data import (\n",
    "    GeoJSONVectorSource, RasterioCRSTransformer, \n",
    "    RasterizedSource, ClassInferenceTransformer)\n",
    "\n",
    "img_uri = 's3://azavea-research-public-data/raster-vision/examples/spacenet/RGB-PanSharpen_AOI_2_Vegas_img205.tif'\n",
    "label_uri = 's3://azavea-research-public-data/raster-vision/examples/spacenet/buildings_AOI_2_Vegas_img205.geojson'\n",
    "\n",
    "crs_transformer = RasterioCRSTransformer.from_uri(img_uri)\n",
    "vector_source = GeoJSONVectorSource(\n",
    "    label_uri,\n",
    "    crs_transformer,\n",
    "    vector_transformers=[ClassInferenceTransformer(default_class_id=1)])\n",
    "\n",
    "rasterized_source = RasterizedSource(\n",
    "    vector_source,\n",
    "    background_class_id=0,\n",
    "    # Normally we'd pass in the RasterSource's extent, but we don't have that here.\n",
    "    bbox=vector_source.bbox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d374fb9b-4d8c-4e3b-8979-16896eb27504",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 400, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chip = rasterized_source[:400, :400]\n",
    "chip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d9aab390-e9a4-4707-ade8-3331b768a31b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.matshow(chip)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "6850b013e1f3bd5bc88fc148f23f814e4d2f79564e8ea88f16f3069ee54d6960"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
